Cancer is a disease that involves changes in genetic and/or epigenetic structures which are transferable to subsequent generations of neoplastic progeny. Cancer cells gain a selective growth advantage over normal cells by accumulating specific genetic alterations. Many types of cancer involve multiple genetic alterations. The alterations typically occur in at least two groups of genes, protooncogenes and tumor suppressor genes. In many tumors the neoplastic process follows a multi-event genetic pathway involving the accumulation of an increasing number of genetic alterations. Specific patterns of genetic evolution have been associated with certain cancers and more aggressive neoplastic behavior.
Development of a neoplasia is thought to start with the clonal expansion of a single cell carrying an inheritable change in DNA that provides a growth/survival advantage. Any cell of this original clone may acquire additional inheritable changes, some of which could provide further survival advantages and give rise to more rapidly growing sub-clones.
Modern molecular technology has made it possible to identify many genetic alterations in human tissue. For example, techniques exist to detect the inactivation of both alleles of tumor-suppressor genes in human tumors. This could occur, for example through mutation of one allele and deletion of genetic material containing the other. Alteration of gene dosage (copy number alteration) of tumor-suppressor genes and protooncogenes are detectable across the entire genome through recent developments in genome-wide methodologies. Examples of these methodologies are described in:    Ishkanian A, et al., A Tiling Resolution DNA Microarray with Complete Coverage of the Human Genome. Nature Genetics 36(3):299-303, 2004. and    Chi B, et al. A software tool for the visualization of whole genome array CGH data. BMC Bioinformatics 5:13, 2003.Integrated analysis of genetic alterations and expression changes can be performed to identify genes that cause certain cancers. Examples of such integrated analysis are described in:    Lockwood W W et al., Integrative genomic and gene expression analysis of NSCLC identifies subtype-specific signatures of pathway disruption. IASLC 12th World Conf, Seoul Korea, Sep. 2-6, 2007; and    Chari R, et al. SIGMA: A System for Integrative Genomic Microarray Analysis of Cancer Genomes BMC Genomics 7:324, 2006.
The ability to measure inheritable alterations across the entire genome of a lesion has resulted unprecedented amounts of data being available from individual cancers. Many have expounded that this expansive genetic information coupled with knowledge will lead to an era of effective personalized treatment, i.e. the detail with which one can interrogate the genetic building blocks of an individual cancer should lead to treatment specifically targeting the genetic events supporting the neoplastic tissue. However these genome-wide tests usually require 100 ng to 3,000 ng of DNA (10's of thousands to 1,000's of thousands of cells worth of material) to work reliably and are usually costly and labor intensive to perform.
Current genome-wide tests have the additional disadvantages that they can be insufficiently sensitive to detect some cancers. The clonal population of dangerous (leading to patient's mortality or morbidity) cells may be very few in number, below that detectible by existing genome-wide technologies, or may be masked by the surrounding non-lethal clones and infiltrating normal cells. Subdividing a lesion into subsets small enough that the DNA of a dangerous clonal population is no longer masked would result in too many genome wide analyses to be economically viable or practical. In addition, a frequent characteristic of developing lesions is genetic instability. Even if genome-wide test fails to identify a particular dangerous clonal population in a neoplasm, the dangerous clonal population may develop soon if precursor cells are present.
Genome-wide testing also fails to take into consideration genetic heterogeneity within tumors. The tissue making up individual tumors tends to be genetically heterogeneous. This occurs because of the mechanisms by which cancer cells grow and develop and also because invasive tumors almost always harbor some genetically normal cells intermixed to varying degrees with the tumor cells. Intra-tumor genetic heterogeneity has been reported in many types of cancers. The cells in a neoplasia that has reached the invasive stage are frequently genetically unstable and are prone to a high rate of mutation due to loss of check point effectiveness and loss of effective DNA repair mechanisms. Thus the genetic make up of cells and groups of clonally related cells can vary dramatically within an individual tumor.
Intra-tumor heterogeneity has important clinical implications. The extent of clonal heterogeneity can be an indicator of the lesion/patient current and future behavior.
IHC (‘immunohistochemistry’) and FISH (‘fluorescence in situ hybridization’) are methodologies which can be applied to detect gene copy number alteration (amplification and deletions) and altered gene expression/protein levels. IHC and FISH Are described, for example, in Theodosiou Z, et al. Automated analysis of FISH and immunohistochemistry images: A review. Cytometry Part A Published Online 71A:(7):439-450, 2007.
FISH involves hybridizing DNA probes to chromosomes. The DNA probes include components that fluoresce under appropriate illumination. Whether or not a chromosome within a cell includes a particular genetic sequence can be determined by observing whether or not a probe for the sequence has hybridized to the chromosome. FISH enables the detection, analysis, and quantification of specific numerical and structural characteristics within cell nuclei. FISH may be used to detect DNA deletions, translocations and amplifications. As such, FISH has application in studying genetic disorders, chromosomal abnormalities and characteristic underlying genetic features of tumors. Some applications of FISH involve the use of multiple probes that hybridize to different DNA sequences. The probes may fluoresce with different colors to facilitate distinction between them. Example techniques for multi-color FISH are described in:    Liehr T, et al. Multicolor-FISH Approaches for the Characterization of Human Chromosomes in Clinical Genetics and Tumor Cytogenetics, Current Genomics 3:213-235, 2002.    Liehr T et al. Multicolor FISH probe sets and their applications, Histol Histopathol 19(1):229-37, 2004.FISH has proven to be as accurate as Southern blot analysis, while allowing the measurement of the fraction of altered cells and the heterogeneity within a given cell population.
One problem with FISH, especially where the tissue is in thin sections is that truncation artefacts can cause FISH signals which should be observed to be missing.
Immunohistochemistry (“IHC”) is a technique that uses antibodies to stain proteins in situ. IHC allows the identification of cells with specific molecular phenotypes.
FISH or IHC results are typically evaluated in a semi-qualitative fashion by human observers. The reading of FISH images is a difficult task since manual dot scoring over a large number of nuclei and over different tissue samples is time consuming and fatiguing. Also, the results can be subjective and observer-dependent.
Raimondo F, et al. Automated Evaluation of Her-2/neu Status in Breast Tissue From Fluorescent In Situ Hybridization Images. IEEE Transactions on Image Processing 14(9):1288-1299, 2005 describe a semi-automated system for analyzing FISH signals for the evaluation of Her-2/neu Status. The system uses image processing software to display the different color channels of a FISH image and apply thresholds for nuclei segmentation. However, the counting of dots in a semi-automatic manner remains impractical procedure for a pathologist, since it requires user intervention for excluding poorly segmented, overlapping, clustered or infiltrating non neoplastic cells. Quantitative analysis is usually done at the field level in which the number of cells with in the field is estimated and the amount of the marker (IHC or FISH spots) measured over the same field and an average score per cell calculated.
IHC biomarkers may be quantified by manual (visual) inspection, usually by a pathologist. Expression of IHC biomarkers is often scored on an ordinal 0-3 scale (in which 0=no staining, 1=weak staining, 2=moderate staining, and 3=strong staining). In some cases the scoring is combined with a scored interpretation of the markers' overall distribution. At best, manual inspection is semi-quantitative, reducing biomarker expression—which generally occurs in nature as a continuous, normal distribution to an ordinal scale. Visual inspection can also be confounded by the inherently subjective nature of human observation, affected by context (e.g. factors such as the amount of tumour present, background staining, and stromal staining). These issues can lead to undesirable inter- and intra-observer variability. In some cases, subtle sub-populations cannot be reliably identified using manual analysis.
The combination of IHC and computer-assisted image analysis systems provides the possibility of objective and reproducible quantification of the IHC staining. A first step in the quantitative analysis is imaging the field of view under a microscope. Different imaging modalities are currently in use, including three-color RGB cameras, monochrome cameras with specific wavelength filters, and multi-spectral imaging systems. Once the image of the field of view is captured, analysis of IHC images is performed, usually, in a semi-automated way with the aid of image analysis gene spatial software.
Commercially available image analysis software such as ACIS™, Ariol™ and Scanscope™ are reported to be able to successfully extract from images of IHC samples information such as average staining intensity within a region of interest and percentage of positive pixels. However, use of these systems typically requires significant operator intervention to set parameters such as thresholds for defining positive and negative areas. Although these software packages claim to perform cell-counting according to morphological and color criteria as well, it seems these features are not validated in published studies and are not typically used (see, for example, Cregger M, et al. Immunohistochemistry and quantitative analysis of protein expression. Arch Pathol Lab Med 130:1026-1030, 2006).
Emily M, et al. Spatial correlation of gene expression measures in tissue microarray core analysis. Journal of Theoretical Medicine 6(1):33-39, 2005 measured the protein expression of DARPP-32 using IHC in a series of 31 patients from a series of 132 breast cancer patients to differentiate between patients which remained disease free after 5 years and those with recurrence or death within 5 years. They showed that a while a mean measure of the expression of DARPP-32 could detect the bad prognosis patients 83% of the time it did so with a poor specificity of 44%. This is in contrast with a specific cell-by-cell spatial correlation measure which demonstrated the same detection rate of 83% while maintaining a specificity of 76%.
There remains a need for practical semi-automated and automated methods and apparatus capable of providing information about tumors and other neoplastic tissues.
Tumor growth, prognosis, and metastasis are dependent on multiple interactions of tumor cells with homeostatic factors in the micro-environment of the neoplasia within the host. Examples of factors that correlate to outcomes include:                tumor aneuploidy (which is strongly associated with poor outcomes).        specific genetic alterations, such as p53 deletion, cMYC amplification, EFGR amplification, etc.        expressions of estrogen and progesterone.        etc.There remains a need for methods and apparatus that can identify genetic and molecular signatures/profiles identifying even small populations of dangerous cells across entire lesions in a high throughput fashion without excessive false positives.        